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Manoj Govindassamy edited comment on HDFS-12042 at 6/27/17 7:43 PM:
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[~misha@cloudera.com],
Looks good overall. Can we also please update {{TestSnapshot}} or {{TestSnapshotDeletion}}
to cover the cases like {{diff}] toggling between null and not null during {{addDiff()}} and
{{deleteSnapshotDiff()}} operation ?
Nit: Few places we are using {{createDiffsIfNeeded()}} and in few other places we are doing
a direct null check and returning null. Though it is contextually right, I am trying to assess
the usefulness of that check. If a file/dir has snapshot enabled, then most likely its going
to have the diffs. So, will the code be simpler if we just call {{createDiffsIfNeeded}} like
in other places? But, this might lead to creating empty arraylist when actually not needed?
was (Author: manojg):
[~misha@cloudera.com],
Looks good overall. Can we also please update {{TestSnapshot}} or {{TestSnapshotDeletion}}
to cover the cases like {{diff}] toggling between null and not null during {{addDiff()}} and
{{deleteSnapshotDiff()}} operation ?
Nit: Few places we are using {{createDiffsIfNeeded()}} and in few other places we are doing
a direct null check and returning null. Though it is contextually right, I am trying to assess
the usefulness of that check. If a file/dir has snapshot enabled, then most likely its going
to have the diffs. So, will the code be simpler if we just call {{createDiffsIfNeeded}} like
in other places?
> Lazy initialize AbstractINodeDiffList#diffs for snapshots to reduce memory consumption
> --------------------------------------------------------------------------------------
>
> Key: HDFS-12042
> URL: https://issues.apache.org/jira/browse/HDFS-12042
> Project: Hadoop HDFS
> Issue Type: Improvement
> Reporter: Misha Dmitriev
> Assignee: Misha Dmitriev
> Attachments: HDFS-12042.01.patch, HDFS-12042.02.patch, HDFS-12042.03.patch
>
>
> When snapshot diff operation is performed in a NameNode that manages several million
HDFS files/directories, NN needs a lot of memory. Some of that memory is wasted due to suboptimal
data structures, such as empty or under-populated ArrayLists, etc. Analyzing one heap dump
with jxray (www.jxray.com), we observed the following problems with data structures:
> {code}
> 9. BAD COLLECTIONS
> Total collections: 99,707,902 Bad collections: 88,799,760 Overhead: 9,063,898K (18.2%)
> Top bad collections:
> Ovhd Problem Num objs Type
> -------------------------------------------------
> 3,056,014K (6.1%) small 29435572 j.u.ArrayList
> 2,641,373K (5.3%) 1-elem 21837906 j.u.ArrayList
> 864,215K (1.7%) 1-elem 5291813 j.u.TreeSet
> 808,456K (1.6%) 1-elem 3045847 j.u.HashMap
> 602,470K (1.2%) empty 18549109 j.u.ArrayList
> 441,563K (0.9%) empty 4356975 j.u.TreeSet
> 373,088K (0.7%) empty 5297007 j.u.HashMap
> 270,324K (0.5%) small 931394 j.u.HashMap
> {code}
> The data structures created by HDFS code that suffer from the above problems are, in
particular:
> {code}
> 4,228,182K (8.5%): j.u.ArrayList: 19412263 of small 2,111,087K (4.2%), 12932408 of
1-elem 1,717,585K (3.4%), 12784310 of empty 399,509K (0.8%)
> <-- org.apache.hadoop.hdfs.server.namenode.snapshot.FileDiffList.diffs <--
org.apache.hadoop.hdfs.server.namenode.snapshot.FileWithSnapshotFeature.diffs <-- org.apache.hadoop.hdfs.server.namenode.INode$Feature[]
<-- org.apache.hadoop.hdfs.server.namenode.INodeFile.features <-- org.apache.hadoop.hdfs.server.blockmanagement.BlockInfo.bc
<-- org.apache.hadoop.util.LightWeightGSet$LinkedElement[] <-- org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap$1.entries
<-- org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap.blocks <-- org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap$1.entries
<-- org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap.blocks <-- org.apache.hadoop.hdfs.server.blockmanagement.BlockManager.blocksMap
<-- org.apache.hadoop.hdfs.server.blockmanagement.BlockManager$BlockReportProcessingThread.this$0
<-- j.l.Thread[] <-- j.l.ThreadGroup.threads <-- j.l.Thread.group <-- Java Static:
org.apache.hadoop.fs.FileSystem$Statistics.STATS_DATA_CLEANER
> {code}
> and
> {code}
> 575,557K (1.2%): j.u.ArrayList: 4363271 of 1-elem 409,056K (0.8%), 2439001 of small
166,482K (0.3%)
> <-- org.apache.hadoop.hdfs.server.namenode.INodeDirectory.children <-- org.apache.hadoop.util.LightWeightGSet$LinkedElement[]
<-- org.apache.hadoop.util.LightWeightGSet.entries <-- org.apache.hadoop.hdfs.server.namenode.INodeMap.map
<-- org.apache.hadoop.hdfs.server.namenode.FSDirectory.inodeMap <-- org.apache.hadoop.hdfs.server.namenode.FSNamesystem.dir
<-- org.apache.hadoop.hdfs.server.namenode.FSNamesystem$NameNodeResourceMonitor.this$0
<-- org.apache.hadoop.util.Daemon.target <-- org.apache.hadoop.hdfs.server.namenode.FSDirectory.inodeMap
<-- org.apache.hadoop.hdfs.server.namenode.FSNamesystem.dir <-- org.apache.hadoop.hdfs.server.namenode.FSNamesystem$NameNodeResourceMonitor.this$0
<-- org.apache.hadoop.util.Daemon.target <-- j.l.Thread[] <-- j.l.ThreadGroup.threads
<-- j.l.Thread.group <-- Java Static: org.apache.hadoop.fs.FileSystem$Statistics.STATS_DATA_CLEANER
> {code}
> There are several different reference chains that all lead to FileDiffList.diffs or INodeDirectory.children.
The total percentage of memory wasted by these data structures in the analyzed dump is about
12%. By creating these lists lazily and/or with capacity that better matches their actual
size, we should be able to reclaim a significant part of these 12%.
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